ABSTRACT ADPKD is the most prevalent inherited renal disease, accounting for 4% of the ESRD population. Detection of cysts utilizing renal imaging has been the most common method of diagnosis of this disease. More importantly, there is at present no definitive way to predict which patients will progress most rapidly, independent of symptomatic therapy and blood pressure control, and who will have a more benign course. This is of paramount importance due to the fact that, due to the myriad mutations associated with the disease, the course is highly variable. Additionally, in the current era in which we are at the cusp of discovering and validating new therapeutics, such as the vasopressin inhibitors, it will be essential to segregate patients into those likely to need such potentially toxic therapy from those who will do well without intervention. An earlier NIH grant from this group was utilized to work out the logistics of optimal collection of samples; we showed unequivocally that day-to-day variability was minimal, and that fasting samples were optimal. Using materials from the HALT trial, in which fasting blood and urine were collected systematically and have been banked, we will perform non-targeted metabolomics analysis on plasma to determine which metabolites can differentiate rapid from slow ADPKD progressors. We will perform discovery (Aim 1) and validation (Aim 3) experiments using HALT samples so, by the end of the study, we can be quite confident of a sensitive and specific test for rapid ADPKD progressors. In addition, we will use pathway and network analysis (Aim 2) to discover novel metabolites which were not identified in the first analysis and which indicate new pathophysiological pathways and perspectives and which will lead to heretofore unrecognized targets for therapy. Successful completion of these experiments will result in a major advance in prognostication, pathophysiology, as well as, ultimately, the selection of optimal treatment regimens for ADPKD. Ours will be the first described use of metabolomics in human cystic kidney disease and one of the first to successfully exploit this technology in any renal disease. Furthermore, our work will serve as a model for using metabolomics to glean pathway and network data from a variety of hereditary diseases.